Systems and methods of determining tread depth

ABSTRACT

There is provided a computerized system comprising a processing unit and associated memory configured to obtain a three-dimensional dataset informative of at least part of a tread of a tire, and determine, using the three-dimensional dataset, data informative of tread depth of the tire.

TECHNICAL FIELD

The presently disclosed subject matter relates, in general, to the fieldof tire inspection, and more specifically, to methods and systems fortread depth estimation.

BACKGROUND

A tire generally includes a tread in which a plurality of grooves ispresent. The tread of a tire refers to the rubber on its circumferencethat makes contact with the road or the ground.

Tread depth, which can be defined e.g. as a distance between the bottomof a groove (for example, the deepest groove) and the top of the treadrubber, impacts vehicle stability and road safety.

Measurement of tread depth is currently mostly performed manually. e.g.with a tire tread depth gauge. However, such manual inspection is notonly costly and time consuming, but also prone to inspection errors andvariations caused by specific personnel performing the inspection.

There is therefore a need to propose new systems and methods to measuretread depth.

GENERAL DESCRIPTION

In accordance with a first aspect of the presently disclosed subjectmatter, there is provided a computerized system comprising a processingunit and associated memory configured to obtain a three-dimensionaldataset informative of at least part of a tread of a tire, anddetermine, using the three-dimensional dataset, data informative oftread depth of the tire.

In addition to the above features, the system according to this aspectof the presently disclosed subject matter can optionally comprise one ormore of features (i) to (xix) below, in any technically possiblecombination or permutation:

-   -   i. the system is configured to obtain at least two images of a        tire acquired at different viewing angles, wherein each of the        two images is informative of a tread of the tire, and generate,        using the two images, the three-dimensional dataset informative        of at least part of the tread based on the two images;    -   ii. the system is configured to determine data informative of        tread depth at various locations around the tread, wherein the        locations are spread along a portion of a total circumference of        the tread which corresponds to at least 5% of the total        circumference of the tread;    -   iii. the system is configured to determine data informative of        tread depth at various locations around the tread, wherein the        three-dimensional dataset is obtained based on data acquisition        of the tread which captures, in a single acquisition, data        informative of at least 5% of the total circumference of the        tread;    -   iv. the system is configured to determine, for at least one        groove of the tread, data informative of tread depth for at        least 100 different locations along a bottom of the groove;    -   v. the three-dimensional dataset comprises a plurality of        points, wherein at least some of the plurality of points have a        position in the three-dimensional dataset which depends on at        least one of (1) a viewing angle of the tread by a device,        and (2) a curvature of the tread, wherein the system is        configured to generate a corrected height for at least some of        the plurality of points which is independent of at least one        of (1) and (2) according to a criterion;    -   vi. the system is configured to generate, using the        three-dimensional dataset, a map informative of height profile        of at least part of the tread, and determine, using the map,        data informative of tread depth of the tire;    -   vii. at least one groove present in the tread, which is        represented as a curved portion in the three-dimensional        dataset, is represented as a substantially straight portion in        the map;    -   viii. generating the map includes unwrapping the        three-dimensional dataset, or data informative thereof;    -   ix. the system is configured to identify, in the map, at least        one area corresponding to a sidewall of at least one groove        present in the tread based on a direction orthogonal to said        area in the map.    -   x. the system is configured to project at least part of the        three-dimensional dataset along a predefined axis, and fit a        predefined shape to a representation of the tread in the        three-dimensional dataset;    -   xi. the predefined shape includes a cylinder, or a toroid;    -   xii. the system is configured to use a relationship enabling        unwrap of the predefined shape into a surface to generate the        map;    -   xiii. the system is configured to determine at least one surface        in the three-dimensional dataset of the tread which is        substantially flat according to a criterion, determine a local        direction orthogonal to the surface, and determine data        informative of tread depth of the tire using the local        direction;    -   xiv. the system is configured to generate, for each of a        plurality of points of said surface, a corrected height with        respect to said local direction, and determine data informative        of tread depth of the tire using the corrected height of each of        a plurality of points of said surface;    -   xv. the system is configured to determine a plurality of given        surfaces of the tread in the three-dimensional model, wherein        each given surface is substantially flat according to a        criterion, determine, for each given surface, a given local        direction orthogonal to said given surface and determine data        informative of tread depth of the tire using given local        directions determined for the given surfaces;    -   xvi. the system is configured to obtain a training set        comprising, for each training sample of the training set, an        image of a tire and data informative of tread depth of the tire,        and feed the training set to a machine learning module, to train        the machine learning module to estimate, based on an image of a        tire, tread depth of the tire;    -   xvii. each training sample comprises a single image of a tire        and data informative of tread depth of the tire;    -   xviii. the system is configured to obtain a single image of a        given tire and to estimate, using the machine learning module        after its training, tread depth of the given tire; and    -   xix. at least one image used to generate the three-dimensional        dataset is acquired by an imaging device from a first angle        relative to a longitudinal direction perpendicular to a surface        of the tread, and wherein the tire is illuminated by an        illumination device from a second angle relative to the        horizontal direction, wherein the illumination device and the        imaging device are positioned so as to have the first angle        being smaller than the second angle.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method comprising using the computerizedsystem as described above (the system can optionally comprise one ormore of features (i) to (xix) above) to determine data informative oftread depth of a tire.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a processing unit andassociated memory, cause the processing unit and associated memory toperform operations in accordance with said method.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a computerized system comprising a processingunit and associated memory configured to obtain a training set includinga plurality of training samples, wherein each given training sample ofthe training set comprises at least one image of a given tire, and datainformative of tread depth of the given tire, and feed the training setto a machine learning module, to train the machine learning module toestimate, based on an image of a tire, tread depth of the tire.

In addition to the above features, the system according to this aspectof the presently disclosed subject matter can optionally comprise one ormore of features (xx) to (xxiii) below, in any technically possiblecombination or permutation:

-   -   xx. a given training sample of the training set comprises at        least two images of a given tire acquired at different angles        and data informative of tread depth of the given tire, wherein        each of the two images is informative of a tread of the given        tire, wherein the computerized system is configured to feed the        training set to the machine learning module, to train the        machine learning module to estimate, based on an at least two        images of a tire acquired at different angles, tread depth of        the tire;    -   xxi. at least one given training sample of the training set        comprises a single image of a given tire and data informative of        tread depth of the given tire, wherein the computerized system        is configured to feed the training set to the machine learning        module, to train the machine learning module to estimate, based        on a single image of a tire acquired at different angles, tread        depth of the tire;    -   xxii. for at least one given training sample associated with a        given tire, determination of data informative of tread depth of        the given tire comprises obtaining a three-dimensional dataset        informative of at least part of a tread of a tire, and        determining, using the three-dimensional dataset, data        informative of tread depth of the tire; and    -   xxiii. the system is configured to obtain a single image of a        tire and to estimate, using the machine learning module after        its training, tread depth of the tire.

In addition to the above features, the system according to this aspectof the presently disclosed subject matter can optionally be configuredto obtain a training set comprising one or more training samples,wherein at least one training sample comprises data informative of treaddepth of a given tire obtained using the system in accordance with thefirst aspect of the presently disclosed subject matter (the system canoptionally comprise one or more of features (i) to (xix) above).

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method comprising using the computerizedsystem as described above (the system can optionally comprise one ormore of features (xx) to (xxiii) above) to determine data informative oftread depth of a tire.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a processing unit andassociated memory, cause the processing unit and associated memory toperform operations in accordance with said method.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a computerized system comprising a processingunit and associated memory configured to obtain at least one image of atire, wherein the at least one image is informative of a tread of thetire, feed the at least one image to a trained machine learning module,and estimate, using the machine learning module, tread depth of thetire.

According to some embodiments, the image is a single image of the tire.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a method comprising using the computerizedsystem as described above to determine data informative of tread depthof a tire.

In accordance with other aspects of the presently disclosed subjectmatter, there is provided a non-transitory computer readable mediumcomprising instructions that, when executed by a processing unit andassociated memory, cause the processing unit and associated memory toperform operations in accordance with said method.

The proposed solution enables automatized measurement of tread depth.

According to some embodiments, the proposed solution is efficient,flexible, lightweight, and simple to implement.

According to some embodiments, the proposed solution enablesdetermination of tread depth of a vehicle while it is travelling.

According to some embodiments, the proposed solution improves accuracyof determining tread depth.

According to some embodiments, the proposed solution enables inspectinga large portion of the tread of a tire to determine tread depth.

According to some embodiments, the proposed solution enables measurementof groove depth with hundreds or thousands of measurements per groove onmultiple grooves, thus improving measurement accuracy and reliability.

According to some embodiments, the proposed solution remotely measurestread depth and does not require installation of a hardware system onthe vehicle.

According to some embodiments, the proposed system is installed on aside of an inspection lane without need for expensive installation belowground.

According to some embodiments, the proposed solution enables tocorrectly measure tread depth of any tread groove pattern (beyondvertical treads).

BRIEF DESCRIPTION OF THE DRAWINGS

In order to understand the invention and to see how it can be carriedout in practice, embodiments will be described, by way of non-limitingexamples, with reference to the accompanying drawings, in which:

FIG. 1 schematically illustrates a functional block diagram of systemwhich is operative to determine data informative of tread depth of atire of a vehicle.

FIG. 2 schematically illustrates an architecture enabling acquiringimages of a tire of a vehicle, wherein the images are usable todetermine tread depth of the tire.

FIG. 2A schematically illustrates a variant of the architecture of FIG.2 .

FIG. 3 schematically illustrates a generalized flowchart of a method ofdetermining tread depth of a tire of a vehicle.

FIG. 3A schematically illustrates a generalized flowchart of a method ofdetermining tread depth of a tire of a vehicle, which includesgenerating a map informative of height profile of the tread.

FIG. 4 illustrates two images of a tire acquired at different angles.

FIG. 5 illustrates an image of a tread of a tire.

FIG. 5A illustrates a map informative of height profile of a tread of atire.

FIG. 5B schematically illustrates height profile along a given slice ofthe tread, generated based on the map of FIG. 5A.

FIG. 6 schematically illustrates a generalized flowchart of a method ofgenerating a three-dimensional dataset informative of a tread using twoimages of the tire.

FIG. 7 schematically illustrates a generalized flowchart of a method ofgenerating a map informative of height profile of a tread of a tire.

FIG. 7A schematically illustrates a projection of a three-dimensionaldataset of informative of a tread along a predefined axis.

FIG. 7B schematically illustrates a toroid.

FIG. 7C schematically illustrates a generalized flowchart of a method ofdetermining tread depth of a tire of a vehicle, based on a localanalysis of the direction of the tread.

FIG. 7D schematically illustrates a non-limitative example of the methodof FIG. 7C.

FIG. 8 schematically illustrates a method of training a machine learningmodel to estimate tread depth of a tire based on one or more images ofthe tire.

FIG. 9 schematically illustrates a method of estimating, using a machinelearning module, tread depth of a tire based on one or more images ofthe tire.

FIG. 9A schematically illustrates an architecture in which a machinelearning module estimates tread depth of a tire based on a single imageof the tire.

FIG. 9B schematically illustrates an architecture in which a machinelearning module estimates tread depth of a tire based on at least twoimages of the tire.

DETAILED DESCRIPTION OF EMBODIMENTS

In the following detailed description, numerous specific details are setforth in order to provide a thorough understanding of the invention.However, it will be understood by those skilled in the art that thepresently disclosed subject matter can be practiced without thesespecific details. In other instances, well-known methods have not beendescribed in detail so as not to obscure the presently disclosed subjectmatter.

Unless specifically stated otherwise, as apparent from the followingdiscussions, it is appreciated that throughout the specification,discussions utilizing terms such as “obtaining”, “generating”,“determining”, “converting”, “unwrapping”, “projecting”, “fitting”,“feeding” or the like, refer to the action(s) and/or process(es) of aprocessing unit that manipulate and/or transform data into other data,said data represented as physical data, such as electronic, quantitiesand/or said data representing the physical objects.

The term “processing unit” covers any computing unit or electronic unitwith data processing circuitry that may perform tasks based oninstructions stored in a memory, such as a computer, a server, a chip, aprocessor, etc. It encompasses a single processor or multipleprocessors, which may be located in the same geographical zone or may,at least partially, be located in different zones and may be able tocommunicate together.

Embodiments of the presently disclosed subject matter are not describedwith reference to any particular programming language. It will beappreciated that a variety of programming languages can be used toimplement the teachings of the presently disclosed subject matter asdescribed herein.

FIG. 1 is a schematic representation of an embodiment of a system 100,which is configured for automatic tread depth estimation of one or moretires of a vehicle.

System 100 includes a processing unit 101 and an associated memory 105.According to some embodiments, system 100 can include e.g. an outputunit 130 (e.g. a screen) and an interface 140 (e.g. an hardwareinterface enabling a user to provide commands, such as a keyboard),operatively coupled to the processing unit 101 and the associated memory105. The processing unit 101 and the associated memory 105 are operativeto perform various methods, such as the methods described with referenceto FIGS. 3, 6, 7, 8 and 9 . The processing unit 101 can be configured toexecute one or more functional modules in accordance withcomputer-readable instructions implemented on a non-transitorycomputer-readable memory, such as memory 105.

The processing unit 101 and associated memory 105 are operativelycoupled to one or more devices 135. As explained hereinafter, dataprovided by device 135 enables to determine a three-dimensionalrepresentation of a tire, and in particular, of a tread of the tire.According to some embodiments, device 135 includes one or more imagingdevice(s) 110. Each imaging device 110 can correspond e.g. to a camera(such as a digital camera with image and/or video recordingfunctionalities). In some embodiments, device 135 can include otherdevices 125, such as a RADAR, a LIDAR, a structured light 3D scanner, a3D laser scanner, etc.

Data communication between the processing unit 101 (and associatedmemory 105) and the device(s) 135 can be performed using e.g. wireand/or wireless communication.

Device(s) 135 can be part of system 100 or can be external to system 100and therefore can be configured to communicate data with system 100.

According to some embodiments, imaging device(s) 110 includes one ormore stereo cameras. A stereo camera includes two or more lenses with aseparate image sensor or film frame for each lens, which enablescapturing/generating stereoscopic images (corresponding to two slightlyoffset images of an object).

According to some embodiments, at least two imaging devices 110 areprovided, which are located in close proximity with a relative offset(e.g. horizontal offset) between them. The two imaging devices 110 canacquire images of at least one tire, e.g. simultaneously.

According to some embodiments, system 100 includes (or is operativelycoupled with) an illumination device 120. Illumination device 120 isoperative to illuminate a region in which a tire of the vehicle islocated, thereby facilitating acquisition of an image of the tire byimaging device 110.

According to some embodiments, illumination device 120 can include e.g.one or more light sources which produce a light focused towards theregion of interest. The illumination device 120 can be located in closeproximity to the imaging device 110 (e.g., positioned on the same sideof the tire as the imaging device 110) and is configured to provideillumination covering the Field of View (FOV) of the imaging device 110so as to enable images to be captured, e.g. at high resolution andquality. In some cases, the imaging device 110 and/or the illuminationdevice 120 can be attached to a supporting structure, such as one polepositioned on at least one side of the inspection lane. The imagingdevice and/or the illumination device can be attached to the supportingstructure at an appropriate height and/or angle in relation to the tireto be inspected.

Imaging device 110 is configured to acquire image(s) of at least onetire (or more) of a vehicle.

In some cases, the vehicle can be a moving vehicle and images of thetire(s) are acquired while the vehicle is moving on an inspection lane.In some other cases, the tires to be inspected are stationary tires,either stand-alone on the inspection lane, or associated with astationary vehicle. In cases where the tire to be inspected is arotating tire of a moving vehicle, the image acquisition can betriggered by an external sensing device which can detect thepresence/approach of the vehicle (such as, e.g., road loop, Infra-red(IR) beam, VMD, etc.).

Imaging device(s) 110 are arranged at a location which enables acquiringimages of at least one tire of a vehicle.

According to some embodiments, imaging device(s) 110 are located, atleast partially, underground (this configuration is not depicted in thedrawings). For example, a cavity, covered by a transparent shield, canbe present in an inspection lane (e.g. in the road) to accommodateimaging device(s) 110. When the vehicle reaches a position correspondingto the location of the cavity (as mentioned, acquisition can beperformed either during motion of the vehicle, or when the tire/vehicleis stationary), imaging device(s) 110 acquire images(s) of at least onetire of the vehicle. Acquiring images of a tire with an above-groundimaging device (as in FIGS. 2 and 2A) enables acquisition of a largerportion of the tire.

According to some embodiments, a first subset of one or more imagingdevices is located on a first side of the vehicle (or of the tire to beinspected), and/or a second subset of one or more imaging devices islocated on a second side (opposite to the first side) of the vehicle (orof the tire to be inspected). Tire images from both sides of the vehiclecan be simultaneously acquired and processed. A specific embodiment ofthis configuration will now be described with reference to FIG. 2 .

One or more imaging devices 210 ₁, 210 ₂ can be located (e.g. mounted orotherwise situated) on at least one side of tires 212 ₁, 212 ₂ to beinspected (e.g. on at least one side of an inspection lane 250 on whichvehicle 240 is located or travels) and configured to acquire one or moreimages of the tire. According to certain embodiments, taking the tire212, for example (the same applies to tire 212 ₂), imaging device 210,is positioned/oriented to face the tire from a first angle 270, relativeto a longitudinal direction 245 perpendicular to the surface of the tiretread, and the one or more images are acquired by imaging device 210 ₁from the first angle 270 ₁. In some embodiments, the longitudinaldirection 245 can be parallel to a travelling direction 260 of thevehicle 240 and/or to the longitudinal axis of the vehicle 240.

In some embodiments, an illumination device 220 is positioned/orientedto illuminate the tire from a second angle 280 ₁ relative to thelongitudinal direction 245. The imaging device 210 ₁ and theillumination device are positioned so as to have the first angle 270 ₁being smaller than the second angle 280 ₁.

In some embodiments, since the illumination device 220 is shedding lightfrom the side of the tire, and the tread blocks part of the light, itnaturally causes a shadow section and an illuminated section at thebottom and/or the sidewall of the groove of the tire. Since the imagingdevice is positioned with a smaller angle with respect to the tire ascompared to the illumination device, the image as acquired can capturethe entire illuminated section and at least part of the shadow section.This configuration facilitates determination of disparity between twoimages of the tire. This is however not mandatory.

Similarly, imaging device 210 ₂ is positioned/oriented to face the tire212 ₁ from a first angle 270 ₂ relative to the longitudinal direction245 perpendicular to the surface of the tire tread, and the one or moreimages are acquired by imaging device 210 ₁ from the first angle 270 ₁.The imaging device 210 ₂ and the illumination device 220 are positionedso as to have the first angle 270 ₂ being smaller than the second angle280 ₁.

In some embodiments one or more imaging devices 201 ₃, 201 ₄ (e.g. oneor cameras) and an associated illumination device 220 ₁ can be locatedon another side of the inspection lane 250 (e.g. right side of theinspection lane 250). Relative position and/or orientation of theimaging device(s) with respect to an associated illumination device canbe selected to be similar to the configuration described in FIG. 2 .This configuration is therefore not described again. This enablesacquiring an image of tire(s) 213 ₁, 213 ₂ on the other side of thevehicle 240.

In some embodiments, and as explained hereinafter, a single imagingdevice can be used (e.g. a single imaging device per side of theinspection lane or per side of the vehicle) to acquire images of tire(s)of the vehicle (e.g. for each side of the vehicle).

In some embodiments, the single imaging device can be locatedunderground.

In other embodiments, and as depicted in FIG. 2A, a single imagingdevice 210 ₁ and an illumination device 220 can be located on a side ofthe inspection lane 250 (left side in FIG. 2A). Although FIG. 2A depictsa vehicle with a single forward left tire 212 and a single forward righttire 213, this applies to multiple tires as in FIG. 2 (e.g. in case of atruck). The single imaging device 210 ₁ can be located relative to theillumination device 220 as explained with reference to FIG. 2 .According to some embodiments, another single imaging device 210 ₃(together with an associated illumination device 220 ₁) can be locatedon another side of the inspection lane 250, which enables acquiringimages of tire(s) 213 located on the other side of the vehicle 240. Thesingle imaging device 210 ₃ can be located relative to the illuminationdevice 220 ₁, as explained with reference to FIG. 2 .

If a device 125 (which is not necessarily a camera, as explained above)is used to acquire three-dimensional data of the tire and/or of thetread, it can be also located e.g. on a side of the tire to be inspectedand/or on the side of the inspection lane.

Attention is now drawn to FIG. 3 . The method of FIG. 3 includesobtaining (operation 300) a three-dimensional dataset informative of atleast part of a tread of a tire. The three-dimensional dataset can beobtained e.g. from device 135 and/or based on data provided by device135. The three-dimensional dataset corresponds e.g. to athree-dimensional point cloud (a set of data points in space enabling 3Dvisualization of the tire, or at least of the tread). Thethree-dimensional dataset provides a three-dimensional representation ofat least part of the tread.

As further explained hereinafter, the three-dimensional dataset can beobtained using various methods. In some embodiments, stereoscopic imageacquisition can be used (see FIG. 6 ). In some embodiments, otherdevices can provide the three-dimensional dataset (or data enabling togenerate the three-dimensional dataset), such as a RADAR, a LIDAR, astructured light 3D scanner, a 3D laser scanner, etc.

In some embodiments, the three-dimensional dataset is informative ofboth the tread and other parts of the tire. Various methods can be usedto differentiate between the tread and other parts of the tire, asexplained hereinafter.

The method of FIG. 3 further includes determining (operation 310), usingthe three-dimensional dataset, data informative of tread depth of thetire.

The term “tread depth” refers to the depth of the grooves (patterns) onthe tire tread. It is a vertical measurement from the top of the tire'srubber (i.e., the surface of the tread—which corresponds to the surfaceof the raised rubber segments, also called tread blocks) to the bottomof the tire's grooves. Tread depth can be measured on several positionsalong the width of the tire.

According to some embodiments, the method enables to determine datainformative of tread depth at various locations around the tread. Insome embodiments, the locations are spread along a portion of a totalcircumference of the tread which corresponds to at least 5% of the totalcircumference of the tread. In some embodiments, the locations arespread along a portion of a total circumference of the tread whichcorresponds to 5% to 20% of the total circumference of the tread (or anyvalue within the range [5%-20%], e.g. at least 10%, at least 15%, etc.).In other words, a large coverage of tire circumference is achieved.

According to some embodiments, the three-dimensional dataset is obtainedbased on data acquisition of the tread which captures, in a singleacquisition, at least 5% of the total circumference of the tread (or anyvalue within the range [5%-20%] of the circumference of the tread). Forexample, as explained hereinafter, in some embodiments, thethree-dimensional dataset can be obtained based on an image acquisitionof the tread. Each image can capture by itself at least 5% (or more asmentioned above) of the total circumference of the tread. This appliesalso to other types of acquisition, such as radar acquisition, etc.

According to some embodiments, tread depth can be determined, for atleast one groove of the tread (or for each of a plurality of grooves ofthe tread), tread depth (or data informative thereof) for at least 100different locations along a bottom of the groove, or for at least 1000different locations, or more. In other words, a high resolution isachieved. In some embodiments, and as mentioned above, these variouslocations are spread along an important fraction of the circumference ofthe tread, such as at least 5% of the total circumference of the tread.

According to some embodiments, since a significant portion of the treadis inspected, it is possible to determine statistical data informativeof the tread depth over the tread, such as average, median, variance,etc. According to some embodiments, additional data, such as treadwidth, can be determined.

As mentioned above, the three-dimensional dataset comprises a pluralityof points. In some embodiments, device 135, which acquires data enablingto generate a three-dimensional dataset of the tread, acquires data fromthe side of the tread. In other words, a viewing angle exists betweenthe line of sight of the device 135 and a reference axis if the tire(see e.g. viewing angles 270 ₁, 270 ₂).

As a consequence, the position of at least some points of the tread inthe three-dimensional dataset (in particular their depth or height) isaffected by this viewing angle.

In addition, since the tread comprises a curvature, the position of atleast some points of tread in the three-dimensional dataset (inparticular their depth or height) is affected by this curvature.

In order to determine tread depth, the method can include determiningthe “true” position (in particular the “true” height—also called depth)of points of the tread in the three-dimensional dataset, which is notaffected by the viewing angle(s) and/or by the curvature of the tread.In particular, and as explained hereinafter, the method can includegenerating a corrected position (e.g. corrected depth or height) for atleast some of the plurality of points of the tread in thethree-dimensional dataset, which is independent of the viewing angle(s)and/or the curvature of the tread, e.g. according to a criterion.Various embodiments are described hereinafter. The criterion reflects towhat extent effect of the viewing angle(s) and/or of the curvature ofthe tread on the position (e.g. depth or height) of the points of thetread is removed when generating the corrected position (e.g. correcteddepth or height). In some embodiments, the criterion can reflect e.g. alevel of required accuracy. In some embodiments, the criterion is suchthat effect of the viewing angle(s) and/or of the curvature in thecorrected depth or height is totally removed.

Attention is drawn to FIG. 3A which depicts a possible embodiment of themethod of FIG. 3 .

The method includes obtaining (operation 300) a three-dimensionaldataset informative of at least part of a tread of a tire.

The method of FIG. 3 further includes (operation 310) using the twoimages to generate a three-dimensional data informative of the tire.Indeed, since two images of the tire acquired at different angles areavailable, it is possible to convert these images into athree-dimensional model using stereoscopic imaging methods.

Attention is drawn to FIG. 3A, which depicts an embodiment of the methodof FIG. 3 .

The method of FIG. 3A comprises operation 300 already described above.

The method of FIG. 3A further includes generating (operation 320), usingthe three-dimensional dataset, a map 500 (see FIG. 5A) informative ofthe height profile of at least part of the tread.

The tread of the tire (also referred to as tire tread, tread ortrack—see e.g. tread 420 in FIG. 4 ) refers to the rubber on itscircumference, which is operative to engage with the road surface. Thetread includes tread blocks (see references 501, 509, 519 and 529 inFIG. 5 ) which are the raised rubber segments that make contact with theroad surface, and grooves (see references 505, 510, 520 and 530 in FIG.5 ) which are channels which run circumferentially and laterally aroundthe tread. The grooves are embedded or molded into the rubber. Thegrooves are designed to allow water to be expelled from beneath the tireand prevent hydroplaning. As tires are used, the tread is worn off,limiting its effectiveness in providing traction, and the vehicle cansuffer from extended braking distances. Shallow tread grooves also makeit harder to control the vehicle in wet weather, and the chance ofhydroplaning increases. To assess tire wear condition, tread depth canbe estimated and provided as a direct indication of the tire'scondition.

According to some embodiments, and as shown in FIGS. 5 and 5A, at leastone groove present in the tread, which is represented as a curvedportion in the three-dimensional dataset (since the groove runs alongthe circumference of the tread), is represented as a substantiallystraight portion in the map 500.

For example (see FIG. 5A), assume that the map 500 is located in a planeX, Y (axis Z corresponds to the height of each point).

Each groove 505, 510, 520 and 530, which corresponds to a curved portionin the tread (and in the three-dimensional dataset), is represented inthe map 500 as a corresponding straight portion 505 ₁, 510 ₁, 520 ₁ and530 ₁ in plane X, Y of the map 500.

In other words, according to some embodiments, the map corresponds to anunwrapped projection of the three-dimensional dataset, or datarepresentative thereof, thereby facilitating measurement of tread depth.

In addition, the map includes data informative of the height (alsocalled depth) of points of the tread, wherein the height (correctedheight, or corrected depth) as measured in the map is independent on theviewing angle and curvature of the tread.

The map provides height profile at various different locations along thecircumference of the tread.

Location of the grooves and/or of the tread blocks of the tread can bedetermined in the map 500.

According to some embodiments, this can include determining areas (alsocalled “blobs”) of the map 500 which correspond to the grooves and areas(also called “blobs”) of the map 500 which correspond to the treadblocks.

For example, grooves can be identified as areas of the map which areassociated with a minimal height (e.g. relative to adjacent areas) inthe map. Therefore, areas 505 ₁, 510 ₁, 520 ₁ and 530 ₁ of the map areidentified as corresponding to the grooves.

Tread blocks of the tread can be identified as areas of the map whichare associated with a maximal height (e.g. relative to adjacent areas)in the map. Therefore, areas 501 ₁, 509 ₁, 519 ₁, 529 ₁ and 539 ₁ of themap are identified as corresponding to the tread blocks.

In order to differentiate between areas of the map corresponding tosidewalls of the grooves, and other areas of the map (corresponding togrooves and tread blocks), the method can include determining, for eachof a plurality of areas of the map, a local direction which isorthogonal to the area (normal direction). Areas corresponding to thegrooves and areas corresponding to the tread blocks have substantiallyparallel normal directions (oriented towards a first direction), andareas corresponding to the sidewalls have a normal direction orientatedtowards a second direction (different from the first direction, thesecond direction being generally orthogonal to the first direction).

Once areas of the map corresponding to the grooves and areas of the mapscorresponding to tread blocks have been identified, it is possible todetermine tread depth (e.g. by determining differences between height ofgrooves and height of tread blocks in the map).

According to some embodiments, tread depth is determined as a differencebetween height of the top of the tread rubber (tread blocks) and heightof the bottom of the tread's grooves.

Data informative of tread depth can be output to the user. In someembodiments, a recommendation indicative of whether the tread (or thetire) should be replaced is provided, based on a comparison betweentread depth as determined for the tire, and required tread depth.

New tires typically have an average tread depth of 8 to 9 millimeters (10/32 to 11/32 inches). Most tire manufacturers consider tires to beworn when one or more of their grooves are worn down to 1.6 millimeters( 2/32 inches), which must be replaced. For safety reasons, in somecases it is recommended to have a minimum tread depth of 3 millimetersfor summer tires, and at least 4 millimeters ( 5/32 inches) for wintertires. These values are not limitative, and can change depending on thegeographical zone, type of car, etc.

Since the map provides height profile for various slices/variouslocations of the tread, it is possible to determine statistical datainformative of the tread depth over the tread, such as average, median,variance, etc. In some embodiments, average tread depth per groove canbe determined. In some embodiments, additional data informative of thetread can be determined, such as average width of the grooves.

As mentioned above, it is possible to determine data informative oftread depth for various locations along the circumference of the tread.

In some embodiments, tread depth can be determined for various slices ofone or more grooves.

For a given slice (along a given direction parallel to axis X) of agroove in the map, a difference between height of the groove, andadjacent prominent tread blocks of the tread rubber can be determinedusing the map, thereby providing local value of tread depth. For atleast one groove which runs along a circumference of the tread, the mapis informative of a depth of the groove for various slices of saidcircumference. This is shown schematically for a limited number ofslices 550, 560 and 570 of the tread, for which it is possible todetermine height profile in the map 500. This example is not limitativeand other slices of the tread can be analyzed using the map.

A non-limitative example of height profile of a given slice (along agiven direction parallel to axis X in the map) of the tread isillustrated in FIG. 5B. In this example, distance 580 providesinformation on tread depth associated with the first groove at the givenslice, distance 590 provides information on tread depth associated withthe second groove at the given slice, etc.

Attention is now drawn to FIG. 6 .

As mentioned with reference to FIG. 3 (see operation 300), athree-dimensional dataset informative of at least part of the tread of atire can be obtained. FIG. 6 depicts an embodiment of a method togenerate this dataset, using stereoscopic images.

The method includes obtaining (operation 600) includes obtaining atleast two images of a tire acquired at different angles, wherein each ofthe two images is informative of a tread of the tire. Assume a firstimage and a second image are obtained. The first image has been acquiredwith a first acquisition/viewing angle (angle between a line of sight ofthe imaging device which acquired the image and a reference axis of thetire), and the second image has been acquired with a secondacquisition/viewing angle (angle between a line of sight of the camerawhich acquired the image and the reference axis of the tire), whereinthe first acquisition/viewing angle differs from the secondacquisition/viewing angle.

According to some embodiments, the two images of the tire are acquiredsimultaneously (e.g. by two different cameras which are e.g.horizontally offset), or with a time offset that can be disregarded. Thetwo images can be acquired and processed using e.g. system 100 describedabove.

FIG. 4 illustrates an example of two images which can be obtained atoperation 600. As visible in FIG. 4 , there is an offset between the twoimages 400, 410 due to the fact that the two images have been acquiredat different angles of acquisition (in other words, each image isacquired with a different line of sight).

The method includes determining (operation 605) disparity between thetwo images. Disparity is informative of difference in coordinates ofsimilar features/points within the two images. Disparity is calculatedusing standard stereoscopic imaging techniques of the art.

The method includes generating (operation 610), based on the disparity,a three-dimensional dataset (e.g. 3D point cloud) informative of atleast part of the tread.

For example, assuming that the optical axes of the two imaging devicesare parallel, the depth Z of a point in the three-dimensional dataset isgiven by:

$Z = \frac{f \cdot b}{d}$

In this equation, “f” is the focal length, “b” is the horizontal offsetbetween the two imaging devices, and “d” is the disparity for givencorresponding pixels Various methods can be used to determine thethree-dimensional dataset (see e.g.courses.cs.washington.edu/courses/cse455/09wi/Lects/lect16.pdf,incorporated herein in its entirety).

Attention is now drawn to FIG. 7 .

As mentioned with reference to FIG. 3A, the three-dimensional datasetinformative of at least part of the tread can be used to generate a mapinformative of height profile of the tread. FIG. 7 depicts possibleoperations that can be performed to generate this map.

The method of FIG. 7 includes determining (operation 700) a subset ofthe three-dimensional dataset which is informative of the tread(corresponding to the external surface of the tire which is configuredto engage with the road). In other words, it is intended todifferentiate, in the three-dimensional dataset, between the tread andother features of the tire or of the background.

Operation 700 can involve using a machine learning module (which can beimplemented e.g. by processing unit 101 and associated memory 105) whichis trained, using e.g. supervised learning, to differentiate between thetread and other parts of the tire in an image and/or in athree-dimensional dataset informative of the tire. In some embodiments,determination of the areas corresponding to the tread can be performedbefore generation of the three dimensional dataset. For example, if thethree dimensional data set is generated based on two stereoscopicimages, determination of the areas corresponding to the tread can beperformed on at least one of the two stereoscopic two dimensionalimages.

Training of the machine learning module can include feeding a pluralityof images and/or three dimensional datasets of a tire to the machinelearning module, wherein an area corresponding to the tread is markede.g. by an operator (supervised learning).

The machine learning module can implement e.g. a deep learning neuralnetwork, a Convolutional Neural Network (CNN), or other architectures.

The method is however not limited to the use of a machine learningmodule and other suitable methods can be used (e.g. image segmentation,3D surface analysis, random forest decision algorithms, etc.).

The method further includes determining data informative of theorientation of the tread in the three-dimensional dataset. Thisorientation can be determined by computing a direction which isorthogonal to a side of the tread or of the tire (see e.g. direction709) in the three-dimensional dataset. This direction can be computede.g. by averaging a plurality of vectors which are locally orthogonal tothe side of the tread or of the tire in the three-dimensional dataset.This direction provides information on the orientation of the tread orof the tire in the three-dimensional dataset.

It is then possible to project (e.g. rotate) the three-dimensionaldataset along a predefined axis. For example, the predefined axis can bee.g. a vertical axis orthogonal to ground. In other words, thethree-dimensional dataset informative of the tread undergoes a spatialtransformation. It is as if the tread has been disposed as lying on theground. A non-limitative example of operation 710 is illustrated in FIG.7A, in which the three-dimensional dataset 705 of the tread is projectedalong axis Z, to obtain projected three-dimensional dataset 708.

Once the orientation of the three-dimensional dataset of the tread hasbeen fixed, the method can include determining a radius of the tread.The radius is useful to unwrap the three-dimensional dataset, asexplained hereinafter.

Determining radius of the tread can include fitting (operation 720) apredefined shape to a representation of the tread in thethree-dimensional dataset (e.g. in the projected dataset 708). Asmentioned above, location of the tread in the three-dimensional datasethas been identified at operation 700.

According to some embodiments, the predefined shape corresponds to acylinder. Fitting a cylinder to the representation of the tread can beperformed using a fitting algorithm (the parameter to be found is theradius of the cylinder). According to some embodiments, the predefinedshape corresponds to a toroid. Fitting a toroid to the representation ofthe tread can be performed using a fitting algorithm (the parameters tobe found are the radius of revolution of the toroid, and the radius ofthe circular section of the toroid). These examples are not limitativeand other shapes can be used.

The method further includes generating (operation 730) a map informativeof height profile of at least part of the tread.

As mentioned above, this can include unwrapping the three-dimensionaldataset (e.g. after it has been projected along a reference axis, asexplained with reference to operation 710), or data informative thereof.

In particular, operation 730 can include using a relationship enablingunwrap of the predefined shape into a surface to generate the map. Thisrelationship defines a spatial transformation enabling projection of thepredefined shape into a surface. This spatial transformation can beviewed, at least in some embodiments, as a conformal mapping.

A non-limitative example of a method of projecting (unwrapping) acylinder into a surface is provided hereinafter (in addition, the methodalso converts the depth/height coordinate of each point of thethree-dimensional dataset).

${{u( {x,y} )} = {{Re}( \frac{( {x + {iy}} )^{2} + R^{2}}{x + {iy}} )}}{{v( {x,y} )} = {{Im}( \frac{( {x + {iy}} )^{2} + R^{2}}{x + {iy}} )}}{{s(z)} = {R{arc}{\sin( \frac{z}{R} )}}}$

In these equations, (x, y, z) correspond to the tree-dimensionalcoordinates of a point of the three-dimensional dataset, and (u, v, s)correspond to the coordinates of a corresponding point (after thetransformation) in the map (u and v correspond to the coordinates in theplane of the map and s correspond to the height or depth). R correspondsto the radius of the cylinder.

A non-limitative example of a method of projecting (unwrapping) a toroidinto a surface is provided hereinafter (in addition, the method alsoconverts the depth/height coordinate of each point of thethree-dimensional dataset).

${x = {{R \cdot ( {{\sin(t)} - {( {t - \theta} ){\cos(t)}}} )} + {\frac{r \cdot {\cos(\varphi)}}{\sqrt{1 + {( {t - \theta} )^{2}( {1 - s} )^{2}}}}( {{\sin(t)} - {( {t - \theta} )( {1 - s} ){\cos(t)}}} )}}}{y = {{R \cdot ( {{\cos(t)} + {( {t - \theta} )^{2}( {1 - s} )^{2}}} )} + {\frac{r \cdot {\cos(\varphi)}}{\sqrt{1 + {( {t - \theta} )^{2}( {1 - s} )^{2}}}}( {{\cos(t)} + {( {t - \theta} )( {1 - s} ){\sin(t)}}} )}}}{z = {r \cdot {\sin(\varphi)}}}{t = {{( {\pi - \theta} )s} + \theta}}{0 \leq \theta \leq {2\pi}}{0 \leq s \leq 1}$

In these equations, R is the outer radius of the toroid, r is the innerradius of the toroid, s is a free parameter, θ is an angle in the planeof the toroid and φ is an angle in a cross-sectional plane of the toroid(see FIG. 7B).

Attention is now drawn to FIG. 7C, which depicts another method ofdetermining tread depth. The method can include determining (operation740—similar to operation 700 in FIG. 7 ) a subset of thethree-dimensional dataset which is informative of the tread(corresponding to the external surface of the tire which is configuredto engage with the road). The method further includes (operation 750)determining at least one surface (or a plurality of surfaces 780 ₁, 780₂, etc.—see FIG. 7D) in the three-dimensional dataset informative of thetread which is substantially flat according to a criterion. Thecriterion can define a maximal deviation which can be accepted betweenthe surface and a perfectly flat surface. The criterion can define amaximal deviation such as it does not significantly impact the treaddepth measurement (e.g. corresponding to an error which is lower than0.2 mm, this value being not limitative).

The partition of the three-dimensional dataset of the tread intosurfaces (780 ₁, 780 ₂, etc.) for local analysis can be performed withnegative, zero or positive overlap between adjacent surfaces. Selectionof the surfaces can be made to reduce calculation time while achievingthe required accuracy.

The method further includes, for each surface (780 ₁, 780 ₂, etc.),determining (operation 760) a local direction 790 ₁, 790 ₂, etc. whichis orthogonal to the surface (local normal direction). Each localdirection is informative of a local tread surface orientation.

The method further includes (operation 770) determining data informativeof tread depth of the tire using the local direction (or the pluralityof local directions).

Operation 770 can include, for each surface, generating, for each of aplurality of points of the surface, a corrected height (also calledcorrected depth) with respect to the local direction. In other words,the corrected height (also called corrected depth) is expressed in areferential linked to the local direction. This enables to obtain acorrected height (also called corrected depth) which is not affected bythe viewing angle of the device 135 (which provides thethree-dimensional dataset informative of the tread, or data enablinggeneration of this three-dimensional dataset) and by the curvature ofthe tread. The corrected height (also called corrected depth)corresponds to an output of a product of a vector corresponding to theheight (also called depth) measured in the three-dimensional dataset(which is viewed by the device 135) and a vector corresponding to thelocal direction which is orthogonal to the surface (as determined atoperation 760).

Operation 770 can further include, for each surface, determining one ormore areas of the surface corresponding to groove(s) and one or moreareas of the surface corresponding to tread block(s). Various methodshave been described above with reference to FIG. 3A (based on asegmentation of the height of the points) and can be used similarly.

Once area(s) corresponding to groove(s) and area(s) corresponding to thetread block(s) have been identified, data informative of tread depth canbe determined based on a difference between height (along the localnormal direction) of points belonging to grooves and height (along thelocal normal direction) of points belonging to the tread blocks. Asexplained above, tread depth can be computed for various locations alongthe tread. Similarly, various statistical data informative of treaddepth can be computed.

Attention is now drawn to FIG. 8 .

The method of FIG. 8 enables training a machine learning module toestimate tread depth of a tire based on at least one image of the tire.In some embodiments, the machine learning module is trained to estimatetread depth of a tire based on a single image of the tire.

The machine learning module can be implemented e.g. by the processingunit 101 and associated memory 105 (see machine learning module 108 inFIG. 1 ). This is however not limitative, and in some embodiments, themachine learning module can be implemented using a different processingunit and associated memory.

The machine learning module can include a network with a plurality oflayers organized according to an architecture such as a Deep NeuralNetwork, Convolutional Neural Network (CNN) architecture, RecurrentNeural Network architecture, Recursive Neural Networks architecture,etc.

The method includes obtaining (operation 800) a training set. Thetraining set includes a plurality of training samples, wherein eachtraining sample includes at least one image of a given tire, and datainformative of tread depth of the given tire.

In some embodiments, each training sample includes a single image of thegiven tire.

Data informative of tread depth of the given tire (for a given trainingsample) can be obtained using the various methods described above. Forexample, in some embodiments, tread depth of the given tire was obtainedusing at least two images of the tire, generating a three-dimensionaldataset informative of the tire, and generating a map informative ofheight profile of the tread (see e.g. the method of FIG. 3 ).

In some embodiments, data informative of tread depth of the given tire(for a given training sample) can be obtained using other methodsdescribed above, or methods including manual methods such as by using atread depth gauge.

The method further includes feeding (operation 810) the training set tothe machine learning module, to train the machine learning module toestimate, based on an image of a tire, tread depth of the tire.

Training of the machine learning module can include determiningweighting and/or threshold values of the network, which can be initiallyselected prior to training, and can be further iteratively adjusted ormodified during training to achieve an optimal set of weighting and/orthreshold values in the trained machine learning module. After eachiteration, a difference can be determined between the actual outputproduced by the machine learning module (predicted tread depth) and thetarget output associated with the respective training sample (measuredtread depth). The difference can be referred to as an error value.Training can be determined to be complete when a cost functionindicative of the error value is less than a predetermined value or whena limited change in performance between iterations is achieved.

If the training set includes training samples each comprising a singleimage of a tire, then the machine learning module is trained toestimate, based on a single image of a tire, tread depth of the tire.

If the training set includes training samples each comprising at leasttwo images of a tire acquired at different angles, then the machinelearning module is trained to estimate, based on two images of a tireacquired at different angles, tread depth of the tire. As mentionedabove, the two images of the tire are acquired simultaneously, or with atime offset which can be disregarded.

Attention is now drawn to FIG. 9 .

Assume that a machine learning module has been trained, as explainedwith reference to FIG. 8 . A trained machine learning module istherefore available.

The method of FIG. 9 includes obtaining (operation 900) an image of atire of a vehicle, wherein the image is informative of a tread of thetire. The method further includes estimating/predicting (operation 910),by the trained machine learning module, tread depth of the tire, or datainformative thereof.

If the trained machine learning module has been trained based ontraining samples, each including a single image of a tire, then it issufficient to obtain a single image of the tire. This single image canbe acquired using e.g. the architecture involving of FIG. 2A. This ishowever not limitative, and other architectures can be used, such as anarchitecture including an underground imaging device, or otherarchitectures.

This is shown in FIG. 9A, in which a single image 920 of a tire is fedto the trained machine learning module 9081, which outputs an estimation930 of tread depth of the tire, or data informative thereof.

If the trained machine learning module has been trained based ontraining samples, each including two images of a tire, then during usageof the trained machine learning module in a prediction mode (estimationmode), two images are fed to the machine learning module. As mentioned,the two images are generally two images of the tire which are eachinformative of the tread of the tire and which have been acquiredsimultaneously, or with a time offset which can be disregarded, and attwo different angles. Acquisition of the two images can rely e.g. on thearchitecture of FIG. 2 . This is, however, not limitative, and otherarchitectures can be used (see e.g. above other examples).

This is shown in FIG. 9B, in which two images 920, 920 of a tire are fedto the trained machine learning module 908 ₂, which outputs anestimation 930 of tread depth of the tire, or data informative thereof.

The invention contemplates a computer program being readable by acomputer for executing at least part of one or more methods of theinvention. The invention further contemplates a machine-readable memorytangibly embodying a program of instructions executable by the machinefor executing at least part of one or more methods of the invention.

It is to be noted that the various features described in the variousembodiments can be combined according to all possible technicalcombinations.

It is to be understood that the invention is not limited in itsapplication to the details set forth in the description contained hereinor illustrated in the drawings. The invention is capable of otherembodiments and of being practiced and carried out in various ways.Hence, it is to be understood that the phraseology and terminologyemployed herein are for the purpose of description and should not beregarded as limiting. As such, those skilled in the art will appreciatethat the conception upon which this disclosure is based can readily beutilized as a basis for designing other structures, methods, and systemsfor carrying out the several purposes of the presently disclosed subjectmatter.

Those skilled in the art will readily appreciate that variousmodifications and changes can be applied to the embodiments of theinvention as hereinbefore described without departing from its scope,defined in and by the appended claims.

1. A computerized system comprising a processing unit and associatedmemory configured to: obtain a three-dimensional dataset informative ofat least part of a tread of a tire, and determine, using thethree-dimensional dataset, data informative of tread depth of the tire.2. The system of claim 1, configured to: obtain at least two images of atire acquired at different viewing angles, wherein each of the twoimages is informative of a tread of the tire, and generate, using thetwo images, the three-dimensional dataset informative of at least partof the tread based on the two images.
 3. The computerized systemaccording to claim 1, configured to determine data informative of treaddepth at various locations around the tread, wherein the locations arespread along a portion of a total circumference of the tread whichcorresponds to at least 5% of the total circumference of the tread. 4.The computerized system according to claim 1, configured to determinedata informative of tread depth at various locations around the tread,wherein the three-dimensional dataset is obtained based on dataacquisition of the tread which captures, in a single acquisition, datainformative of at least 5% of the total circumference of the tread. 5.The computerized system according to claim 1, configured to determine,for at least one groove of the tread, data informative of tread depthfor at least 100 different locations along a bottom of the groove. 6.The computerized system according to claim 1, wherein: thethree-dimensional dataset comprises a plurality of points, wherein atleast some of the plurality of points have a position in thethree-dimensional dataset which depends on at least one of: (1) aviewing angle of the tread by a device, and (2) a curvature of thetread, wherein the computerized system is configured to generate acorrected height for at least some of the plurality of points which isindependent of at least one of (1) and (2) according to a criterion. 7.The computerized system according to claim 1, configured to: generate,using the three-dimensional dataset, a map informative of height profileof at least part of the tread, and determine, using the map, datainformative of tread depth of the tire.
 8. The computerized systemaccording to claim 7, wherein at least one groove present in the tread,which is represented as a curved portion in the three-dimensionaldataset, is represented as a substantially straight portion in the map.9. The computerized system according to claim 7, wherein generating themap includes unwrapping the three-dimensional dataset, or datainformative thereof.
 10. The computerized system according to claim 7,configured to: project at least part of the three-dimensional datasetalong a predefined axis, and fit a predefined shape to a representationof the tread in the three-dimensional dataset.
 11. The computerizedsystem according to claim 10, wherein the predefined shape includes: acylinder, or a toroid.
 12. The computerized system according to claim 1,configured to: determine at least one surface in the three-dimensionaldataset of the tread which is substantially flat according to acriterion, determine a local direction orthogonal to the surface, anddetermine data informative of tread depth of the tire using the localdirection.
 13. The computerized system according to claim 12, configuredto: generate, for each of a plurality of points of said surface, acorrected height with respect to said local direction, and determinedata informative of tread depth of the tire using the corrected heightof each of a plurality of points of said surface.
 14. The computerizedsystem according to claim 1, configured to: obtain a training setcomprising, for each training sample of the training set, an image of atire and data informative of tread depth of the tire, and feed thetraining set to a machine learning module, to train the machine learningmodule to estimate, based on an image of a tire, tread depth of thetire.
 15. The computerized system according to claim 14, wherein eachtraining sample comprises a single image of a tire and data informativeof tread depth of the tire.
 16. The computerized system according toclaim 14, configured to obtain a single image of a given tire and toestimate, using the machine learning module after its training, treaddepth of the given tire.
 17. The computerized system according to claim1, wherein at least one image used to generate the three-dimensionaldataset is acquired by an imaging device from a first angle relative toa longitudinal direction perpendicular to a surface of the tread, andwherein the tire is illuminated by an illumination device from a secondangle relative to the horizontal direction, wherein the illuminationdevice and the imaging device are positioned so as to have the firstangle being smaller than the second angle.
 18. A computerized systemcomprising a processing unit and associated memory configured to: obtaina training set including a plurality of training samples, wherein eachgiven training sample of the training set comprises at least one imageof a given tire, and data informative of tread depth of the given tire,and feed the training set to a machine learning module, to train themachine learning module to estimate, based on an image of a tire, treaddepth of the tire.
 19. The computerized system according to claim 18,wherein at least one of (i) and (ii) is met: (i) a given training sampleof the training set comprises at least two images of a given tireacquired at different angles and data informative of tread depth of thegiven tire, wherein each of the two images is informative of a tread ofthe given tire, wherein the computerized system is configured to feedthe training set to the machine learning module, to train the machinelearning module to estimate, based on an at least two images of a tireacquired at different angles, tread depth of the tire; (ii) at least onegiven training sample of the training set comprises a single image of agiven tire and data informative of tread depth of the given tire,wherein the computerized system is configured to feed the training setto the machine learning module, to train the machine learning module toestimate, based on a single image of a tire acquired at differentangles, tread depth of the tire.
 20. The computerized system accordingto claim 18, wherein, for at least one given training sample associatedwith a given tire, determination of data informative of tread depth ofthe given tire comprises: obtaining a three-dimensional datasetinformative of at least part of a tread of a tire, and determining,using the three-dimensional dataset, data informative of tread depth ofthe tire.
 21. The computerized system according to claim 18, configuredto obtain a single image of a tire and to estimate, using the machinelearning module after its training, tread depth of the tire.
 22. Acomputerized system comprising a processing unit and associated memoryconfigured to: obtain at least one image of a tire, wherein the at leastone image is informative of a tread of the tire, feed the at least oneimage to a trained machine learning module, and estimate, using themachine learning module, tread depth of the tire.
 23. The computerizedsystem according to claim 22, wherein the image is a single image of thetire.